With the development of information technology, more and more people begin to use relevant technology on business intelligence to analyze and process business data to provide powerful support for decision-makers. Also, with the development and application of database technology, the data amount stored in a database rocketed high from mega (M) bytes and gigabytes (G) in the 1980s to current trillion (T) bytes and peta (P) bytes. Meanwhile, query requirements from users also become increasingly complex, which involves not only querying or manipulating one or more pieces of records in a relational table but also performing data analysis and information syntheses on tens of millions of pieces of recorded data in a plurality of tables. However, a transaction processing type relational database system cannot meet all such requirements. For operation and analytical type applications, they cannot meet performance requirements; thus, people always release the restriction on redundancy in a relational database and introduces statistical and integrated data. However, the application logics of such statistical and integrated data are dispersed, random, and unsystematic; thus, the analytical function is limited, inflexible, and difficult to maintain. Many software manufacturers compensate insufficient support from the relational database management system by developing its front end products, attempting to unify dispersed public application logics through a dedicated data integration engine, aided by a more intuitive data access interface, so as to respond to a complex query requirement from a non-professional data processing person in short time.
Business Intelligence (BI) technology processes a great amount of data and reflects information and knowledge in data. Business Intelligence refers to relevant technology, application, etc., which extracts valuable data from existing data of an enterprise so as to help the enterprise to make sensible business operation decisions. The data comprises various kinds of data from the business system of the enterprise itself and other external environments where the enterprise is located. In order to transform data into knowledge, data in a data source is usually populated into a data warehouse through an ETL (Extract-Transform-Load, i.e., a process of data extraction, transformation and loading) model. Then, a data cube is created based on the data in a data warehouse through an OLAP (On-Line Analysis Processing) model, for utilizing data mining to form a statement report and data analysis report.
However, since there are varieties of data sources and processing of ETL model and OLAP model involves a great mount of data, error likely occurs during the BI data processing process. The prior art determines data accuracy by checking the data in the generated report and directly comparing it with the original data in the application system. However, since the data amount in the report is too high, a comprehensible comparison is usually impossible. Besides, even if it is found that the data in the report is inconsistent with the original data in the application system, it is impossible to determine the cause of the problem. The workload for comprehensively checking data in the models and data warehouse is overwhelmingly large, which always needs considerable time to determine the cause of the problem.